Although past research suggests authoritarianism may be a uniquely right-wing phenomenon, the present two studies tested the hypothesis that authoritarianism exists in both right-wing and left-wing contexts in essentially equal degrees. Across two studies, university (n 5 475) and Mechanical Turk (n 5 298) participants completed either the RWA (right-wing authoritarianism) scale or a newly developed (and parallel) LWA (left-wing authoritarianism) scale. Participants further completed measurements of ideology and three domain-specific scales: prejudice, dogmatism, and attitude strength. Findings from both studies lend support to an authoritarianism symmetry hypothesis: Significant positive correlations emerged between LWA and measurements of liberalism, prejudice, dogmatism, and attitude strength. These results largely paralleled those correlating RWA with identical conservative-focused measurements, and an overall effect-size measurement showed LWA was similarly related to those constructs (compared to RWA) in both Study 1 and Study 2. Taken together, these studies provide evidence that LWA may be a viable construct in ordinary U.S. samples.KEY WORDS: left-wing authoritarianism, right-wing authoritarianism, rigidity of the right, ideologyThe concept of left-wing authoritarianism-the idea that political liberals may be subject to the same reliance on simple authority and psychological rigidity as political conservatives-has a controversial history in psychology. While some have argued that left-wing authoritarianism (LWA) is a valid construct (

Integrative complexity is a conceptually unique and very popular measurement of the complexity of human thought. We believe, however, that it is currently being underutilized because it takes quite a bit of time to score. More time-efficient computer-based measurements of complexity that are currently available are correlated with integrative complexity at fairly low levels. To help fill in this gap, we developed a novel automated integrative complexity system designed specifically from the integrative complexity theoretical framework. This new automated IC system achieved an alpha of .72 on the standard integrative complexity coding test. In addition, across nine datasets covering over 1,300 paragraphs, this new automated system consistently showed modest relationships with human-scored integrative complexity (average alpha = .62; average r = .46). Further analyses revealed that this relationship consistently remained significant when controlling for superficial markers of complexity and that the new system accounted for both the differentiation and integration components of integrative complexity. Although the overlap between the automated and human-scored systems is only modest (and thus suggests the continued usefulness of human scoring), it nonetheless provides the best automated integrative complexity measurement to date.

Prior research suggests that liberals are more complex than conservatives. However, it may be that liberals are not more complex in general, but rather only more complex on certain topic domains (while conservatives are more complex in other domains). Four studies (comprised of over 2,500 participants) evaluated this idea. Study 1 involves the domain specificity of a self-report questionnaire related to complexity (dogmatism). By making only small adjustments to a popularly used dogmatism scale, results show that liberals can be significantly more dogmatic if a liberal domain is made salient. Studies 2-4 involve the domain specificity of integrative complexity. A large number of open-ended responses from college students (Studies 2 and 3) and candidates in the 2004 Presidential election (Study 4) across an array of topic domains reveals little or no main effect of political ideology on integrative complexity, but rather topic domain by ideology interactions. Liberals are higher in complexity on some topics, but conservatives are higher on others. Overall, this large dataset calls into question the typical interpretation that conservatives are less complex than liberals in a domain-general way.

Research suggests that the integrative complexity of political rhetoric tends to drop during election season, but little research to date directly addresses if this drop in complexity serves to increase or decrease electoral success. The two present studies help fill this gap. Study 1 demonstrates that, during the Democratic Party primary debates in [2003][2004], the eventual winners of the party nomination showed a steeper drop in integrative complexity as the election season progressed than nonwinning candidates. Study 2 presents laboratory evidence from the most recent presidential campaign demonstrating that, while the complexity of Obama's rhetoric had little impact on college students' subsequent intentions to vote for him, the complexity of McCain's rhetoric was significantly positively correlated with their likelihood of voting for him. Taken together, this research is inconsistent with an unqualified simple is effective view of the complexity-success relationship. Rather, it is more consistent with a compensatory view: Effective use of complexity (or simplicity) may compensate for perceived weaknesses. Thus, appropriately timed shifts in complexity levels, and/or violations of negative expectations relevant to complexity, may be an effective means of winning elections. Surprisingly, mere simplicity as such seems largely ineffective.

Automating integrative complexity is fraught with many challenges. To address these challenges, we discuss the tension between a specificity approach and a more flexible multiple-pass approach, the multifaceted nature of the complexity construct, the gold standard for complexity measurement, the difficulty of human scoring and its consequences for automation, and some ways forward for creating the best complexity measurements. In so doing, we present new data demonstrating (1) initial evidence for the validity of a new automated system for measuring two different forms of complexity (elaborative and dialectical), (2) the danger of constructing measurements in a purely ad hoc fashion that ignores prospective testing, (3) human-to-computer correspondence is in part a function of human-to-human correspondence, (4) human-to-computer correspondence increases systematically as one uses tests with larger units of analysis, and (5) the lack of correspondence of different systems (both human and automated) may occur in part because they were designed for different units of analysis.